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Relevant bibliographies by topics / Agile Data Warehousing

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Academic literature on the topic 'Agile Data Warehousing'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Agile Data Warehousing"

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Rahman, Nayem, Dale Rutz, and Shameem Akhter. "Agile Development in Data Warehousing." International Journal of Business Intelligence Research 2, no. 3 (July 2011): 64–77. http://dx.doi.org/10.4018/jbir.2011070105.

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Traditional data warehouse projects follow a waterfall development model in which the project goes through distinct phases such as requirements gathering, design, development, testing, deployment, and stabilization. However, both business requirements and technology are complex in nature and the waterfall model can take six to nine months to fully implement a solution; by then business as well as technology has often changed considerably. The result is disappointed stakeholders and frustrated development teams. Agile development implements projects in an iterative fashion. Also known as the sixty percent solution, the agile approach seeks to deliver more than half of the user requirements in the initial release, with refinements coming in a series of subsequent releases which are scheduled at regular intervals. An agile data warehousing approach greatly increases the likelihood of successful implementation on time and within budget. This article discusses agile development methodologies in data warehousing and business intelligence, implications of the agile methodology, managing changes in data warehouses given frequent change in business intelligence (BI) requirements, and demonstrates the impact of agility on the business.

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Batra, Dinesh. "Adapting Agile Practices for Data Warehousing, Business Intelligence, and Analytics." Journal of Database Management 28, no. 4 (October 2017): 1–23. http://dx.doi.org/10.4018/jdm.2017100101.

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Business surveys indicate that fewer than 30% of data warehousing and business intelligence (DW/BI) projects meet the stated goals of the budget, schedule, and quality. Agile methods have been suggested as a possible solution, but because of the large size of the typical DW/BI project, it may be difficult to apply the agile values and principles. In this article, the following research questions are raised: Can agile practices be adapted for DW/BI development? What factors influence agile DW/BI development? Six semi-structured interviews were conducted using a questionnaire. The interview transcripts were coded using the grounded theory approach. Eight categories emerged from the analysis: business value, project management, agile development, shared understanding, technological capability, top management commitment, complexity, and organizational culture. Based on the categories, a research framework is proposed. The findings reveal that agile methods are suited for only certain aspects of DW/BI projects and need to be augmented with project management practices.

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Spengler, Helmut, Claudia Lang, Tanmaya Mahapatra, Ingrid Gatz, Klaus A. Kuhn, and Fabian Prasser. "Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation." JMIR Medical Informatics 8, no. 7 (July 21, 2020): e15918. http://dx.doi.org/10.2196/15918.

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Background Modern data-driven medical research provides new insights into the development and course of diseases and enables novel methods of clinical decision support. Clinical and translational data warehouses, such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART, are important infrastructure components that provide users with unified access to the large heterogeneous data sets needed to realize this and support use cases such as cohort selection, hypothesis generation, and ad hoc data analysis. Objective Often, different warehousing platforms are needed to support different use cases and different types of data. Moreover, to achieve an optimal data representation within the target systems, specific domain knowledge is needed when designing data-loading processes. Consequently, informaticians need to work closely with clinicians and researchers in short iterations. This is a challenging task as installing and maintaining warehousing platforms can be complex and time consuming. Furthermore, data loading typically requires significant effort in terms of data preprocessing, cleansing, and restructuring. The platform described in this study aims to address these challenges. Methods We formulated system requirements to achieve agility in terms of platform management and data loading. The derived system architecture includes a cloud infrastructure with unified management interfaces for multiple warehouse platforms and a data-loading pipeline with a declarative configuration paradigm and meta-loading approach. The latter compiles data and configuration files into forms required by existing loading tools, thereby automating a wide range of data restructuring and cleansing tasks. We demonstrated the fulfillment of the requirements and the originality of our approach by an experimental evaluation and a comparison with previous work. Results The platform supports both i2b2 and tranSMART with built-in security. Our experiments showed that the loading pipeline accepts input data that cannot be loaded with existing tools without preprocessing. Moreover, it lowered efforts significantly, reducing the size of configuration files required by factors of up to 22 for tranSMART and 1135 for i2b2. The time required to perform the compilation process was roughly equivalent to the time required for actual data loading. Comparison with other tools showed that our solution was the only tool fulfilling all requirements. Conclusions Our platform significantly reduces the efforts required for managing clinical and translational warehouses and for loading data in various formats and structures, such as complex entity-attribute-value structures often found in laboratory data. Moreover, it facilitates the iterative refinement of data representations in the target platforms, as the required configuration files are very compact. The quantitative measurements presented are consistent with our experiences of significantly reduced efforts for building warehousing platforms in close cooperation with medical researchers. Both the cloud-based hosting infrastructure and the data-loading pipeline are available to the community as open source software with comprehensive documentation.

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Rahman, Nayem. "Lessons from a Successful Data Warehousing Project Management." International Journal of Information Technology Project Management 8, no. 4 (October 2017): 30–45. http://dx.doi.org/10.4018/ijitpm.2017100103.

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This article provides an overview of project management aspects of a data warehouse application implementation. More specifically, the article discusses the project's implementation, challenges faced, and lessons learned. The project was initiated with an objective to redesign the procurement data pipeline of a data warehouse. The data flows from enterprise resource planning (ERP) system to enterprise data warehouse (EDW) to reporting environments. This project was challenged to deliver more quickly to the consumers with improved report performance, and reduced total cost of ownership (TCO) in EDW and data latency. Strategies of this project include providing continuous business value, and adopt new technologies in data extraction, transformation and loading. The project's strategy was also to implement it using some of the agile principles. The project team accomplished twice the scope of previous project in the same duration with a relatively smaller team. It also achieved improved quality of the products, and increased customer satisfaction by improving the reports' response time for management.

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Batra, Dinesh. "Agile values or plan-driven aspects: Which factor contributes more toward the success of data warehousing, business intelligence, and analytics project development?" Journal of Systems and Software 146 (December 2018): 249–62. http://dx.doi.org/10.1016/j.jss.2018.09.081.

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Ahmed, Jeelani, and Dr Muqeem Ahmed. "Big data and semantic web, challenges and opportunities a survey." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 631. http://dx.doi.org/10.14419/ijet.v7i4.5.21174.

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In recent years, vast and complex amounts of data are being created and making it difficult for traditional data processing applications to manage them. The coming of the Internet prompted monstrous spike in the volume of information being made and made accessible. World Wide Web consortium W3C and international standardization body of the web spread the Semantic Web. It is an extended form of current web which provide easier way to search, reuse, combine and share information. In the last few years, major businesses corporations have demonstrated interest in incorporating semantic web technology with big data for added value. Indeed this incorporation has some benefits as well; it increases end-users ability to self-manage data from various sources, it on focuses changing business environments and varying user needs and handles concepts and relationships, manages terminology while connecting different data from varied data sources. For Social Network Analysis (SNA) new methods are needed by combining Big Data and Semantic Web technologies as a way to utilize and add capacities to existing frameworks. Moreover, the fast changing business requirements and latest industry culture of Agile Development needs a robust yet flexible solution for Business Intelligence and by using distributed enterprise level ontologies Data Warehousing can be incorporated. This paper is an attempt to focus on effects of incorporating Big Data with Semantic web, how Semantic Web making Big Data smarter, revisit the Big Data and Semantic Web challenges and opportunities, relationship between them and finally we summarizes with future direction of this integration

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Johnson, Ralph J. "A Comprehensive Research Study Literature Review of EPIC© in Terms of Enabling Healthcare Agility: A Report Card." Journal of Medical Informatics and Decision Making 1, no. 4 (February 20, 2021): 1–21. http://dx.doi.org/10.14302/issn.2641-5526.jmid-21-3739.

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Background As healthcare markets have become more dynamic and turbulent, healthcare organizations have evolved by becoming increasingly “Smart-Agile” in their business practices. Smart-Agility definition-ally ensures success due to its inherent ability to rapidly detect and react appropriately to varied and evolving unclear, complex, and seemingly tumultuous situations and produce high-quality, low-cost goods and services with high customer satisfaction. Thus, there is a vital need for Smart-Agile healthcare IT systems for collection, analyses, and reporting of substantial quantities of healthcare data to inform patient treatment and organizational decisions. EPIC® and its meaningful-use components appear increasingly popular, capturing a majority portion of the healthcare Electronic Healthcare Records (EHR) IT market (>~30%).Yet, there are few, if any, studies reporting on EPIC in terms of Smart-Agility. Aim The intent of this article is to report a systematic review of scientific literature regarding EPIC’s healthcare IT systems meaningful-use features cross-compared with Smart-Agility aspects to produce a positive vs. negative report card—and whether its features are critical vs. non-critical in terms of Smart-Agility. Method Findings reported herein derive from a grounded, iterative review of open-source, peer-reviewed scientific literature following PRISMA. Findings Report card results were mixed. EPIC clearly succeeds and excels (better than average) on Smart-Agile healthcare IT system core aspects that are the most central, critical and valuable in terms of informing healthcare organizations’ decisions and their patients’ care (6 out of 7; B+, -A), specifically: Standardized Data Collection / Connectivity, Real-Time Data Warehousing/Outcome Measures, Enhanced Patient Safety, Patient Tracking and Follow-up (Continuity of Care), Patient Involvement, and Potential Use in Medical Education. The only critical core criterion it failed on was End-User Satisfaction, and some of that appears to dissipate with new users’ software familiarity. Conclusion EPIC provides a solid and relatively inexpensive foundation with great potential for enabling Smart Agility in healthcare organizations with its high-quality collection and management of vast amounts of inter-connected raw data, auto-analysis, and fast report generation. But it does so with hidden costs and inefficiencies. Avenues of further inquiry are suggested.

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Nag, Barin, Chaodong Han, and Dong-qing Yao. "Mapping supply chain strategy: an industry analysis." Journal of Manufacturing Technology Management 25, no. 3 (April 1, 2014): 351–70. http://dx.doi.org/10.1108/jmtm-06-2012-0062.

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Purpose – In manufacturing industries, the levels of inventories at all stages (i.e. raw material, work-in-process and finished goods inventories) indicate the firm's competitive positioning, strategies, internal processes and relationships with suppliers and downstream customers. The authors identify patterns of manufacturing industries based on levels of raw material and finished goods inventories to classify inbound and outbound supply chain strategies. Design/methodology/approach – The authors review literature on supply chain inventory strategy and perform cluster analysis to analyze patterns of manufacturing industries based on manufacturing industry data collected from US Census of Bureau. Following Porter's Five Forces Model, the authors perform in-depth case studies of four representative industries to analyze factors driving supply chain strategies, including industry intensity of rivalry, threat of new entrants, threat of substitutes, bargaining power of suppliers, and bargaining power of buyers. Findings – This study identifies three streams of research on supply chain strategy: Fisher's model and its variations, lean and agile paradigms, and push/pull systems. It finds that whether an industry shows low or high raw materials or finished goods inventories depending on its products, processes, and the dynamics of all forces described in the Five Forces Model. Research limitations/implications – This study is not able to include supplier selection, production strategies, warehousing and distribution, and even product design into the analysis of supply chain strategy due to data limitation. This study classifies industries based on average inventory levels of raw materials and finished goods, while inventory levels and supply chain strategies for specific firms may vary significantly within each industry. Originality/value – This study contributes to the supply chain management literature by providing a parsimonious framework of mapping inbound and outbound supply chain inventory strategies, and the results based on the analyses of all US manufacturing industries provide a baseline picture for supply chain management professionals with manufacturing firms.

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Dissertations / Theses on the topic "Agile Data Warehousing"

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Devarapalli, Surendra. "AGILE BUSINESS INTELLIGENCE DEVELOPMENT CORE PRACTICES." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-17241.

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Today we are in an age of Information. The systems that effectively use the vast amount of data available all over the world and provide meaningful insight (i.e. BI systems) for the people who need it are of critical importance. The development of such systems has always been a challenge as the development is outweighed by change. The methodologies that are devised for coping with the constant change during the system development are agile methodologies. So practitioners and researchers are showing keen interest to use agile strategies for the BI projects development.The research aims to find out how well the agile strategies suit for the development of BI projects. The research considers a case study in a very big organization as BI is organization centric. There by assessing the empirical results that are collected from interviews the author is trying to generalize the results. The results for the research will give an insight of the best practices that can be considered while considering agile strategies and also the practical problems that we may encounter on the journey. The findings have implications for both business and technical managers who want to consider agile strategies for the BI/DW development projects.
Program: Masterutbildning i Informatik

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Řezníček, Miroslav. "Hodnocení projektu Implementace BI v poradenské společnosti." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-114265.

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This diploma thesis deals with the implementation of Business Intelligence in small and medium-sized companies and furthermore focuses on an agile project management and introduces possibilities for this approach. The aim of the project is to present an agile methodology of Business Intelligence implementation and comparison of processes of a real project with the procedures proposed in this methodology and eventually propose improvements. The thesis is divided into three main parts. The first part is an introduction to the theory, in which the concept of Business Intelligence is introduced and important terms as transactional and analytical systems, data warehouse, data mart, etc. are described, being followed by research of previous works focused on Business Intelligence implementation. The introduction to the theory is closed with introducing CRISP-DM methodology for SMEs and agile methodologies as Scrum and Extreme Programming. The objectives of the thesis and some sub-goals are achieved through the analysis of the agile methodology "Agile Data Warehousing for Business Intelligence implementation" in the second part of the thesis. The final main section is a comparison with a real project, in which certain improvements are also proposed for future use. The benefit of this thesis is especially the unusual view at the implementation of Business Intelligence through agile approach and proposed methods for future use in small and medium-sized companies. The benefits are also described particularly for business companies in the area of financial advice.

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Mulder, Susan. "An action research study on the use of Scrum to provide agility in data warehouse development." Diss., University of Pretoria, 2010. http://hdl.handle.net/2263/24561.

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Data warehousing is a new and emerging field. Projects tend to be complex and time consuming. Because of this complexity, teams tend to commit to more than they can deliver. This causes delayed delivery. Applying Agile development styles to data warehousing is one of the alternative methodologies that are being investigated to help teams to accelerate the delivery of business value. Scrum is one of the frameworks that falls within the Agile stream. Scrum focuses on project management and makes use of iterative and incremental development. It tries to deliver the smallest piece of business value the fastest. The paper evaluates the implementation of Scrum in a data warehouse team of a financial investment company. The researcher did an action research study on the team to see if Scrum can be used as a viable alternative framework to bring agility to Data Warehouse development. She examined the changes that the team experienced during and after the implementation of Scrum, focusing on team structure and roles within the teams. The researcher defined a framework to evaluate how well the team implemented Scrum. The researcher also evaluated the quality of work delivered, and the predictability and productivity of the team as metrics to see if Scrum made a difference within the team. The research paper examined why the implementation failed and what issues Scrum highlighted within the team as well as within the way that the company implemented it.
Dissertation (MCom)--University of Pretoria, 2010.
Informatics
unrestricted

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Quiroz, Ñato Jorge Enrique. "Desarrollo de un sistema de inteligencia de negocios en tiempo real utilizando el enfoque Agile Data Warehousing basado en una arquitectura de virtualización de datos para el análisis del tráfico postal de la empresa Servicios Postales del Perú." Bachelor's thesis, Universidad Nacional Mayor de San Marcos, 2018. https://hdl.handle.net/20.500.12672/8184.

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Publicación a texto completo no autorizada por el autor
Desarrolla un sistema de inteligencia de negocio (BI) que resuelva los problemas de análisis y monitoreo del tráfico postal, en el Área de Postal de la empresa de Servicios Postales del Perú, para llevar a cabo un adecuado análisis de información, entregando una herramienta análisis y monitoreo de la información en tiempo real para las áreas de Gerencia Postal. El análisis del tráfico postal es muy importante y para la toma de decisiones de estas áreas, como la apertura de nuevas sucursales, la gestión del personal y creación de nuevos servicios de envíos, la fijación de tarifas de los servicios de envíos, entre otros. Para seleccionar la solución de BI más apta a las necesidades de la empresa, se realiza un análisis comparativo de arquitecturas y metodologías que son usadas para el desarrollo de soluciones de inteligencia de negocio, obteniéndose como resultado el desarrollo de una solución usando la metodología Agile Data Warehousing basado en una arquitectura de virtualización de datos.
Tesis

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Books on the topic "Agile Data Warehousing"

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Agile analytics: A value-driven approach to business intelligence and data warehousing. Upper Saddle River, NJ: Addison-Wesley, 2012.

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Hughes, Ralph. Agile data warehousing project management: Business intelligence systems using Scrum and XP. Waltham, MA: Morgan Kaufmann, 2013.

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Agile Data Warehousing Project Management. Elsevier, 2013. http://dx.doi.org/10.1016/c2011-0-06103-3.

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Agile Data Warehousing for the Enterprise. Elsevier, 2016. http://dx.doi.org/10.1016/c2011-0-06893-x.

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Extreme Scoping An Agile Approach To Enterprise Data Warehousing And Business Intelligence. Technics Publications LLC, 2013.

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Hughes, Ralph. Agile Data Warehousing for the Enterprise: A Guide for Solutions Architects and Project Leaders. Elsevier Science & Technology Books, 2015.

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Book chapters on the topic "Agile Data Warehousing"

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Golfarelli, Matteo, Stefano Rizzi, and Elisa Turricchia. "Sprint Planning Optimization in Agile Data Warehouse Design." In Data Warehousing and Knowledge Discovery, 30–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32584-7_3.

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Hughes, Ralph. "What Is Agile Data Warehousing?" In Agile Data Warehousing Project Management, 3–32. Elsevier, 2013. http://dx.doi.org/10.1016/b978-0-12-396463-2.00001-6.

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Hughes, Ralph. "Adapting Agile for Data Warehousing." In Agile Data Warehousing Project Management, 251–302. Elsevier, 2013. http://dx.doi.org/10.1016/b978-0-12-396463-2.00008-9.

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Hughes, Ralph. "Starting and Scaling Agile Data Warehousing." In Agile Data Warehousing Project Management, 303–44. Elsevier, 2013. http://dx.doi.org/10.1016/b978-0-12-396463-2.00009-0.

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Hughes, Ralph. "Developer Stories for Data Integration." In Agile Data Warehousing Project Management, 175–206. Elsevier, 2013. http://dx.doi.org/10.1016/b978-0-12-396463-2.00006-5.

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Hughes, Ralph. "Iterative Development in a Nutshell." In Agile Data Warehousing Project Management, 33–79. Elsevier, 2013. http://dx.doi.org/10.1016/b978-0-12-396463-2.00002-8.

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Hughes, Ralph. "Streamlining Project Management." In Agile Data Warehousing Project Management, 81–113. Elsevier, 2013. http://dx.doi.org/10.1016/b978-0-12-396463-2.00003-x.

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Hughes, Ralph. "Authoring Better User Stories." In Agile Data Warehousing Project Management, 117–41. Elsevier, 2013. http://dx.doi.org/10.1016/b978-0-12-396463-2.00004-1.

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Hughes, Ralph. "Deriving Initial Project Backlogs." In Agile Data Warehousing Project Management, 143–74. Elsevier, 2013. http://dx.doi.org/10.1016/b978-0-12-396463-2.00005-3.

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Hughes, Ralph. "Estimating and Segmenting Projects." In Agile Data Warehousing Project Management, 207–48. Elsevier, 2013. http://dx.doi.org/10.1016/b978-0-12-396463-2.00007-7.

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